library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(ade4)
library(adegraphics)
##
## Attaching package: 'adegraphics'
##
## The following objects are masked from 'package:ade4':
##
## kplotsepan.coa, s.arrow, s.class, s.corcircle, s.distri,
## s.image, s.label, s.logo, s.match, s.traject, s.value,
## table.value, triangle.class
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.2
library(foreign)
## Warning: package 'foreign' was built under R version 3.2.2
mb5 <- read.csv("C:/Users/Antoine/Desktop/mb5.csv")
df <- mb5
df2 <- na.omit(df)
head(df)
## session numetab patronyme com sensible
## 1 1997 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 2 1998 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 3 1999 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 4 2000 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 5 2001 0750105G COURS DU SOIR POUR ADULTES 75102 SANS OBJET
## 6 2002 0750105G COURS DU SOIR POUR ADULTES 75102 SANS OBJET
## id_etab tot_inscrits psucc p_cs1 p_cs2 p_cs3 p_cs4
## 1 0750105G_1997 111 44.14415 0 2.702703 1.801802 2.7027028
## 2 0750105G_1998 129 55.81395 0 3.875969 11.627907 0.7751938
## 3 0750105G_1999 99 55.55556 0 3.030303 12.121212 5.0505052
## 4 0750105G_2000 94 52.12766 0 2.127660 12.765958 0.0000000
## 5 0750105G_2001 80 48.75000 0 1.250000 7.500000 2.5000000
## 6 0750105G_2002 72 40.27778 0 6.944445 6.944445 11.1111110
## p_cs5 p_cs6 p_cs7 p_cs8 p_cs9 cep tx_btb cep2
## 1 0.9009009 0.000000 0.0000000 0.9009009 90.990990 0 2.040816 0
## 2 6.2015505 0.000000 0.7751938 2.3255813 74.418602 0 0.000000 0
## 3 6.0606060 4.040404 1.0101010 0.0000000 68.686867 0 0.000000 0
## 4 0.0000000 1.063830 1.0638298 1.0638298 81.914894 0 2.040816 0
## 5 2.5000000 5.000000 0.0000000 1.2500000 80.000000 0 2.564103 0
## 6 38.8888890 4.166666 0.0000000 25.0000000 6.944445 0 3.448276 0
## depa ps_es ps_l ps_s tx_btb_es tx_btb_l tx_btb_s
## 1 75 33.33333 53.84615 44.44444 0.00000 4.761905 0
## 2 75 59.52381 61.36364 46.51163 0.00000 0.000000 0
## 3 75 48.48485 61.29032 57.14286 0.00000 0.000000 0
## 4 75 60.71429 45.45454 51.51515 0.00000 6.666666 0
## 5 75 36.00000 61.53846 48.27586 0.00000 6.250000 0
## 6 75 34.61538 58.33333 27.27273 11.11111 0.000000 0
head(df2)
## session numetab patronyme com sensible
## 1 1997 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 2 1998 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 3 1999 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 4 2000 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 5 2001 0750105G COURS DU SOIR POUR ADULTES 75102 SANS OBJET
## 6 2002 0750105G COURS DU SOIR POUR ADULTES 75102 SANS OBJET
## id_etab tot_inscrits psucc p_cs1 p_cs2 p_cs3 p_cs4
## 1 0750105G_1997 111 44.14415 0 2.702703 1.801802 2.7027028
## 2 0750105G_1998 129 55.81395 0 3.875969 11.627907 0.7751938
## 3 0750105G_1999 99 55.55556 0 3.030303 12.121212 5.0505052
## 4 0750105G_2000 94 52.12766 0 2.127660 12.765958 0.0000000
## 5 0750105G_2001 80 48.75000 0 1.250000 7.500000 2.5000000
## 6 0750105G_2002 72 40.27778 0 6.944445 6.944445 11.1111110
## p_cs5 p_cs6 p_cs7 p_cs8 p_cs9 cep tx_btb cep2
## 1 0.9009009 0.000000 0.0000000 0.9009009 90.990990 0 2.040816 0
## 2 6.2015505 0.000000 0.7751938 2.3255813 74.418602 0 0.000000 0
## 3 6.0606060 4.040404 1.0101010 0.0000000 68.686867 0 0.000000 0
## 4 0.0000000 1.063830 1.0638298 1.0638298 81.914894 0 2.040816 0
## 5 2.5000000 5.000000 0.0000000 1.2500000 80.000000 0 2.564103 0
## 6 38.8888890 4.166666 0.0000000 25.0000000 6.944445 0 3.448276 0
## depa ps_es ps_l ps_s tx_btb_es tx_btb_l tx_btb_s
## 1 75 33.33333 53.84615 44.44444 0.00000 4.761905 0
## 2 75 59.52381 61.36364 46.51163 0.00000 0.000000 0
## 3 75 48.48485 61.29032 57.14286 0.00000 0.000000 0
## 4 75 60.71429 45.45454 51.51515 0.00000 6.666666 0
## 5 75 36.00000 61.53846 48.27586 0.00000 6.250000 0
## 6 75 34.61538 58.33333 27.27273 11.11111 0.000000 0
df$session <- as.factor(df$session)
df$cep2 <- as.factor(df$cep2)
df$cep <- as.factor(df$cep)
df$depa <- as.factor(df$depa)
attach(df)
df2 <- df[order(id_etab),]
dcs <- df2
df97 <- filter(df, session=="1997")
df98 <- filter(df, session=="1998")
df99 <- filter(df, session=="1999")
df00 <- filter(df, session=="2000")
df01 <- filter(df, session=="2001")
df02 <- filter(df, session=="2002")
df03 <- filter(df, session=="2003")
df04 <- filter(df, session=="2004")
df05 <- filter(df, session=="2005")
df06 <- filter(df, session=="2006")
df07 <- filter(df, session=="2007")
df08 <- filter(df, session=="2008")
df09 <- filter(df, session=="2009")
df10 <- filter(df, session=="2010")
df11 <- filter(df, session=="2011")
df12 <- filter(df, session=="2012")
df13 <- filter(df, session=="2013")
df14 <- filter(df, session=="2014")
df97s <- select(df97, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df98s <- select(df98, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df99s <- select(df99, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df00s <- select(df00, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df01s <- select(df01, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df02s <- select(df02, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df03s <- select(df03, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df04s <- select(df04, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df05s <- select(df05, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df06s <- select(df06, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df07s <- select(df07, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df08s <- select(df08, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df09s <- select(df09, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df10s <- select(df10, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df11s <- select(df11, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df12s <- select(df12, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df13s <- select(df13, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df14s <- select(df14, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 , psucc, tx_btb)
df97i <- select(df97, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df98i <- select(df98, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df99i <- select(df99, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df00i <- select(df00, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df01i <- select(df01, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df02i <- select(df02, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df03i <- select(df03, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df04i <- select(df04, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df05i <- select(df05, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df06i <- select(df06, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df07i <- select(df07, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df08i <- select(df08, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df09i <- select(df09, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df10i <- select(df10, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df11i <- select(df11, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df12i <- select(df12, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df13i <- select(df13, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
df14i <- select(df14, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9 )
wdf97 <- data.frame(scalewt(df97i, wt=df97$tot_inscrits))
wdf98 <- data.frame(scalewt(df98i, wt=df98$tot_inscrits))
wdf99 <- data.frame(scalewt(df99i, wt=df99$tot_inscrits))
wdf00 <- data.frame(scalewt(df00i, wt=df00$tot_inscrits))
wdf01 <- data.frame(scalewt(df01i, wt=df01$tot_inscrits))
wdf02 <- data.frame(scalewt(df02i, wt=df02$tot_inscrits))
wdf03 <- data.frame(scalewt(df03i, wt=df03$tot_inscrits))
wdf04 <- data.frame(scalewt(df04i, wt=df04$tot_inscrits))
wdf05 <- data.frame(scalewt(df05i, wt=df05$tot_inscrits))
wdf06 <- data.frame(scalewt(df06i, wt=df06$tot_inscrits))
wdf07 <- data.frame(scalewt(df07i, wt=df07$tot_inscrits))
wdf08 <- data.frame(scalewt(df08i, wt=df08$tot_inscrits))
wdf09 <- data.frame(scalewt(df09i, wt=df09$tot_inscrits))
wdf10 <- data.frame(scalewt(df10i, wt=df10$tot_inscrits))
wdf11 <- data.frame(scalewt(df11i, wt=df11$tot_inscrits))
wdf12 <- data.frame(scalewt(df12i, wt=df12$tot_inscrits))
wdf13 <- data.frame(scalewt(df13i, wt=df13$tot_inscrits))
wdf14 <- data.frame(scalewt(df14i, wt=df14$tot_inscrits))
wdf97x <- data.frame(scalewt(df97s, wt=df97$tot_inscrits))
wdf98x <- data.frame(scalewt(df98s, wt=df98$tot_inscrits))
wdf99x <- data.frame(scalewt(df99s, wt=df99$tot_inscrits))
wdf00x <- data.frame(scalewt(df00s, wt=df00$tot_inscrits))
wdf01x <- data.frame(scalewt(df01s, wt=df01$tot_inscrits))
wdf02x <- data.frame(scalewt(df02s, wt=df02$tot_inscrits))
wdf03x <- data.frame(scalewt(df03s, wt=df03$tot_inscrits))
wdf04x <- data.frame(scalewt(df04s, wt=df04$tot_inscrits))
wdf05x <- data.frame(scalewt(df05s, wt=df05$tot_inscrits))
wdf06x <- data.frame(scalewt(df06s, wt=df06$tot_inscrits))
wdf07x <- data.frame(scalewt(df07s, wt=df07$tot_inscrits))
wdf08x <- data.frame(scalewt(df08s, wt=df08$tot_inscrits))
wdf09x <- data.frame(scalewt(df09s, wt=df09$tot_inscrits))
wdf10x <- data.frame(scalewt(df10s, wt=df10$tot_inscrits))
wdf11x <- data.frame(scalewt(df11s, wt=df11$tot_inscrits))
wdf12x <- data.frame(scalewt(df12s, wt=df12$tot_inscrits))
wdf13x <- data.frame(scalewt(df13s, wt=df13$tot_inscrits))
wdf14x <- data.frame(scalewt(df14s, wt=df14$tot_inscrits))
row.names(wdf97x) <- df97$numetab
row.names(wdf98x) <- df98$numetab
row.names(wdf99x) <- df99$numetab
row.names(wdf00x) <- df00$numetab
row.names(wdf01x) <- df01$numetab
row.names(wdf02x) <- df02$numetab
row.names(wdf03x) <- df03$numetab
row.names(wdf04x) <- df04$numetab
row.names(wdf05x) <- df05$numetab
row.names(wdf06x) <- df06$numetab
row.names(wdf07x) <- df07$numetab
row.names(wdf08x) <- df08$numetab
row.names(wdf09x) <- df09$numetab
row.names(wdf10x) <- df10$numetab
row.names(wdf11x) <- df11$numetab
row.names(wdf12x) <- df12$numetab
row.names(wdf13x) <- df13$numetab
row.names(wdf14x) <- df14$numetab
row.names(wdf97) <- df97$numetab
row.names(wdf98) <- df98$numetab
row.names(wdf99) <- df99$numetab
row.names(wdf00) <- df00$numetab
row.names(wdf01) <- df01$numetab
row.names(wdf02) <- df02$numetab
row.names(wdf03) <- df03$numetab
row.names(wdf04) <- df04$numetab
row.names(wdf05) <- df05$numetab
row.names(wdf06) <- df06$numetab
row.names(wdf07) <- df07$numetab
row.names(wdf08) <- df08$numetab
row.names(wdf09) <- df09$numetab
row.names(wdf10) <- df10$numetab
row.names(wdf11) <- df11$numetab
row.names(wdf12) <- df12$numetab
row.names(wdf13) <- df13$numetab
row.names(wdf14) <- df14$numetab
lwdfx <- list(wdf97x, wdf98x, wdf99x, wdf00x, wdf01x, wdf02x, wdf03x, wdf04x, wdf05x, wdf06x, wdf07x, wdf08x, wdf09x, wdf10x, wdf11x, wdf12x, wdf13x, wdf14x)
kwdfx <- ktab.list.df(lwdfx)
lwdf <- list(wdf97, wdf98, wdf99, wdf00, wdf01, wdf02, wdf03, wdf04, wdf05, wdf06, wdf07, wdf08, wdf09, wdf10, wdf11, wdf12, wdf13, wdf14)
kwdf <- ktab.list.df(lwdf)
ptakwdfx <- pta(kwdfx, scannf = FALSE, nf=2)
plot(ptakwdfx)
s.class(ptakwdfx$Tli, df$numetab, col=rainbow(100))
s.traject(ptakwdfx$Tli, df$numetab, col=rainbow(100))
mfakwdfx <- mfa(kwdfx, scannf = FALSE, nf=2)
plot(mfakwdfx)
s.class(mfakwdfx$lisup, df$numetab, col=rainbow(100))
s.traject(mfakwdfx$lisup, df$numetab, col=rainbow(100))
mcoakwdfx <- mcoa(kwdf, scannf = FALSE, nf = 2)
plot(mcoakwdfx)
statiskwdfx <- statis(kwdfx, scannf = FALSE, nf = 2)
plot(statiskwdfx)
df2 <- df[order(session),]
head(df2)
## session numetab patronyme com sensible
## 1 1997 0750105G COURS DU SOIR POUR ADULTES 75102 NON
## 19 1997 0750647W TURGOT 75103 NON
## 37 1997 0750648X VICTOR HUGO 75103 NON
## 55 1997 0750651A SIMONE WEIL 75103 NON
## 73 1997 0750652B CHARLEMAGNE 75104 NON
## 91 1997 0750653C SOPHIE GERMAIN 75104 NON
## id_etab tot_inscrits psucc p_cs1 p_cs2 p_cs3
## 1 0750105G_1997 111 44.14415 0.0000000 2.702703 1.801802
## 19 0750647W_1997 181 69.06078 0.0000000 19.889503 29.281769
## 37 0750648X_1997 140 79.28571 0.0000000 12.142858 47.857143
## 55 0750651A_1997 25 64.00000 0.0000000 16.000000 44.000000
## 73 0750652B_1997 218 85.77982 0.0000000 11.467890 59.633026
## 91 0750653C_1997 235 58.29787 0.4255319 11.063829 35.319149
## p_cs4 p_cs5 p_cs6 p_cs7 p_cs8 p_cs9 cep
## 1 2.702703 0.9009009 0.000000 0.000000 0.9009009 90.990990 0
## 19 8.839779 19.3370170 11.602210 1.657458 8.2872925 1.104972 0
## 37 12.857142 10.7142860 5.714286 5.000000 1.4285715 4.285714 0
## 55 16.000000 8.0000000 8.000000 8.000000 0.0000000 0.000000 0
## 73 13.302752 5.0458717 3.211009 0.000000 1.3761468 5.963303 0
## 91 18.297873 17.8723410 8.936171 1.276596 2.1276596 4.680851 0
## tx_btb cep2 depa ps_es ps_l ps_s tx_btb_es tx_btb_l
## 1 2.040816 0 75 33.33333 53.84615 44.44444 0.000000 4.761905
## 19 2.400000 0 75 84.31373 73.07692 60.57692 2.325581 0.000000
## 37 3.603604 0 75 NA 85.33334 72.30769 0.000000 4.687500
## 55 0.000000 0 75 64.00000 NA NA 0.000000 0.000000
## 73 15.508022 0 75 NA 94.87180 83.79888 0.000000 27.027027
## 91 0.000000 0 75 56.48148 65.21739 53.44828 0.000000 0.000000
## tx_btb_s
## 1 0.000000
## 19 3.174603
## 37 2.127660
## 55 0.000000
## 73 12.666667
## 91 0.000000
xptakwdf <- cbind(ptakwdf$Tli, df2)
xmcoakwdf <- cbind(mcoakwdf$Tli, df2)
xptakwdfx <- cbind(ptakwdfx$Tli, df2)
xmcoakwdfx <- cbind(mcoakwdfx$Tli, df2)
pptakwdf <- ggplot(xptakwdf, aes(CS1, CS2))
pptakwdf + geom_path()
pptakwdf + geom_path(aes(colour = session))
pptakwdf + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
pptakwdf + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
pmcoakwdf <- ggplot(xmcoakwdf, aes(Axis1, Axis2))
pmcoakwdf + geom_path()
pmcoakwdf + geom_path(aes(colour = session))
pmcoakwdf + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
pmcoakwdf + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
pptakwdfx <- ggplot(xptakwdfx, aes(CS1, CS2))
pptakwdfx + geom_path()
pptakwdfx + geom_path(aes(colour = session))
pptakwdfx + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
pptakwdfx + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
pmcoakwdfx <- ggplot(xmcoakwdfx, aes(Axis1, Axis2))
pmcoakwdfx + geom_path()
pmcoakwdfx + geom_path(aes(colour = session))
pmcoakwdfx + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
pmcoakwdfx + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
lyc <- read.csv("C:/Users/Antoine/Desktop/lyc.csv")
lyc <- na.omit(lyc)
dcs <- select(lyc, session, patronyme, id_etab, numetab, tot_inscrits, psucc, tx_btb, p_cs1, p_cs2, p_cs3, p_cs4, p_cs5, p_cs6, p_cs7, p_cs8, p_cs9, cep, cep2, codedep, codecom)
attach(dcs)
## The following objects are masked from df:
##
## cep, cep2, id_etab, numetab, p_cs1, p_cs2, p_cs3, p_cs4,
## p_cs5, p_cs6, p_cs7, p_cs8, p_cs9, patronyme, psucc, session,
## tot_inscrits, tx_btb
dcs2 <- dcs[order(id_etab),]
dcs <- dcs2
d97 <- filter(dcs, session=="1997")
d98 <- filter(dcs, session=="1998")
d99 <- filter(dcs, session=="1999")
d00 <- filter(dcs, session=="2000")
d01 <- filter(dcs, session=="2001")
d02 <- filter(dcs, session=="2002")
d03 <- filter(dcs, session=="2003")
d04 <- filter(dcs, session=="2004")
d05 <- filter(dcs, session=="2005")
d06 <- filter(dcs, session=="2006")
d07 <- filter(dcs, session=="2007")
d08 <- filter(dcs, session=="2008")
d09 <- filter(dcs, session=="2009")
d10 <- filter(dcs, session=="2010")
d11 <- filter(dcs, session=="2011")
d12 <- filter(dcs, session=="2012")
d13 <- filter(dcs, session=="2013")
d14 <- filter(dcs, session=="2014")
d97i <- select(d97, p_cs1:p_cs9)
d98i <- select(d98, p_cs1:p_cs9)
d99i <- select(d99, p_cs1:p_cs9)
d00i <- select(d00, p_cs1:p_cs9)
d01i <- select(d01, p_cs1:p_cs9)
d02i <- select(d02, p_cs1:p_cs9)
d03i <- select(d03, p_cs1:p_cs9)
d04i <- select(d04, p_cs1:p_cs9)
d05i <- select(d05, p_cs1:p_cs9)
d06i <- select(d06, p_cs1:p_cs9)
d07i <- select(d07, p_cs1:p_cs9)
d08i <- select(d08, p_cs1:p_cs9)
d09i <- select(d09, p_cs1:p_cs9)
d10i <- select(d10, p_cs1:p_cs9)
d11i <- select(d11, p_cs1:p_cs9)
d12i <- select(d12, p_cs1:p_cs9)
d13i <- select(d13, p_cs1:p_cs9)
d14i <- select(d14, p_cs1:p_cs9)
wd97s <- data.frame(scalewt(d97i, wt=d97$tot_inscrits))
wd98s <- data.frame(scalewt(d98i, wt=d98$tot_inscrits))
wd99s <- data.frame(scalewt(d99i, wt=d99$tot_inscrits))
wd00s <- data.frame(scalewt(d00i, wt=d00$tot_inscrits))
wd01s <- data.frame(scalewt(d01i, wt=d01$tot_inscrits))
wd02s <- data.frame(scalewt(d02i, wt=d02$tot_inscrits))
wd03s <- data.frame(scalewt(d03i, wt=d03$tot_inscrits))
wd04s <- data.frame(scalewt(d04i, wt=d04$tot_inscrits))
wd05s <- data.frame(scalewt(d05i, wt=d05$tot_inscrits))
wd06s <- data.frame(scalewt(d06i, wt=d06$tot_inscrits))
wd07s <- data.frame(scalewt(d07i, wt=d07$tot_inscrits))
wd08s <- data.frame(scalewt(d08i, wt=d08$tot_inscrits))
wd09s <- data.frame(scalewt(d09i, wt=d09$tot_inscrits))
wd10s <- data.frame(scalewt(d10i, wt=d10$tot_inscrits))
wd11s <- data.frame(scalewt(d11i, wt=d11$tot_inscrits))
wd12s <- data.frame(scalewt(d12i, wt=d12$tot_inscrits))
wd13s <- data.frame(scalewt(d13i, wt=d13$tot_inscrits))
wd14s <- data.frame(scalewt(d14i, wt=d14$tot_inscrits))
row.names(wd97s) <- d97$numetab
row.names(wd98s) <- d98$numetab
row.names(wd99s) <- d99$numetab
row.names(wd00s) <- d00$numetab
row.names(wd01s) <- d01$numetab
row.names(wd02s) <- d02$numetab
row.names(wd03s) <- d03$numetab
row.names(wd04s) <- d04$numetab
row.names(wd05s) <- d05$numetab
row.names(wd06s) <- d06$numetab
row.names(wd07s) <- d07$numetab
row.names(wd08s) <- d08$numetab
row.names(wd09s) <- d09$numetab
row.names(wd10s) <- d10$numetab
row.names(wd11s) <- d11$numetab
row.names(wd12s) <- d12$numetab
row.names(wd13s) <- d13$numetab
row.names(wd14s) <- d14$numetab
lwds <- list(wd97s, wd98s, wd99s, wd00s, wd01s, wd02s, wd03s, wd04s, wd05s, wd06s, wd07s, wd08s, wd09s, wd10s, wd11s, wd12s, wd13s, wd14s)
kwds <- ktab.list.df(lwds)
ptads <- pta(kwds, scannf = FALSE, nf=2)
mcoads <- mcoa(kwds, scannf = FALSE, nf=2)
mfads <- mfa(kwds, scannf=FALSE, nf=2)
statisds <- statis(kwds, scannf = FALSE, nf = 2)
plot(ptads)
plot(mcoads)
plot(mfads)
plot(statisds)
kplot(ptads)
kplot(mcoads)
kplot(mfads)
kplot(statisds)
s.class(ptads$Tli, dcs$numetab, col=rainbow(100))
s.traject(ptads$Tli, dcs$numetab, col=rainbow(100))
s.class(mcoads$Tli, dcs$numetab, col=rainbow(100))
s.traject(mcoads$Tli, dcs$numetab, col=rainbow(100))
dcs2 <- dcs[order(session),]
head(dcs2)
## session patronyme id_etab numetab tot_inscrits
## 352 2003 COLBERT 0750673Z_2003 0750673Z 212
## 715 2014 HENRI BERGSON 0750711R_2014 0750711R 133
## 935 2012 GASTON BACHELARD 0770922J_2012 0770922J 197
## 1171 1997 GEORGE SAND 0771663P_1997 0771663P 199
## 1351 1997 JEAN VILAR 0772229E_1997 0772229E 277
## 1481 2008 DE LA MARE CARREE 0772296C_2008 0772296C 163
## psucc tx_btb p_cs1 p_cs2 p_cs3 p_cs4 p_cs5
## 352 68.39623 2.758621 0.000000 9.433962 23.11321 12.735849 18.86792
## 715 67.66917 6.666667 1.503759 7.518797 30.82707 6.766917 24.81203
## 935 85.27919 26.190475 0.000000 5.076142 29.94924 26.395939 18.27411
## 1171 76.38191 3.947369 0.000000 5.025125 29.14573 25.125628 23.11558
## 1351 58.12275 3.105590 1.444043 5.054151 21.29964 18.411552 18.05054
## 1481 67.48467 7.272728 0.000000 4.294478 16.56442 25.766870 22.69939
## p_cs6 p_cs7 p_cs8 p_cs9 cep cep2 codedep codecom
## 352 17.924528 3.301887 11.320755 3.301887 1 0 75 110
## 715 3.007519 3.007519 10.526316 12.030075 1 1 75 119
## 935 9.644671 3.553299 4.568528 2.538071 1 1 77 108
## 1171 12.562814 1.005025 3.015075 1.005025 1 0 77 285
## 1351 22.021660 2.527076 7.220217 3.971119 1 0 77 284
## 1481 17.791410 1.840491 7.361963 3.680982 1 0 77 296
head(ptads$Tli)
## CS1 CS2
## 0750673Z.Ana1 0.402198828 -0.427390043
## 0750711R.Ana1 0.142674638 -0.100310876
## 0770922J.Ana1 0.894347068 -0.009410588
## 0771663P.Ana1 0.704504710 0.006962598
## 0772229E.Ana1 -0.006843246 0.230681280
## 0772296C.Ana1 0.713707749 0.252912577
head(mcoads$Tli)
## Axis1 Axis2
## 0750673Z.Ana1 -0.07772155 0.07455603
## 0750711R.Ana1 -0.02875942 0.02443772
## 0770922J.Ana1 -0.12615731 -0.00558298
## 0771663P.Ana1 -0.08780350 -0.01159594
## 0772229E.Ana1 -0.00757513 -0.05795082
## 0772296C.Ana1 -0.09695404 -0.04464101
xpta <- cbind(ptads$Tli, dcs2)
xmcoa <- cbind(mcoads$Tli, dcs2)
head(xpta)
## CS1 CS2 session patronyme
## 0750673Z.Ana1 0.402198828 -0.427390043 2003 COLBERT
## 0750711R.Ana1 0.142674638 -0.100310876 2014 HENRI BERGSON
## 0770922J.Ana1 0.894347068 -0.009410588 2012 GASTON BACHELARD
## 0771663P.Ana1 0.704504710 0.006962598 1997 GEORGE SAND
## 0772229E.Ana1 -0.006843246 0.230681280 1997 JEAN VILAR
## 0772296C.Ana1 0.713707749 0.252912577 2008 DE LA MARE CARREE
## id_etab numetab tot_inscrits psucc tx_btb
## 0750673Z.Ana1 0750673Z_2003 0750673Z 212 68.39623 2.758621
## 0750711R.Ana1 0750711R_2014 0750711R 133 67.66917 6.666667
## 0770922J.Ana1 0770922J_2012 0770922J 197 85.27919 26.190475
## 0771663P.Ana1 0771663P_1997 0771663P 199 76.38191 3.947369
## 0772229E.Ana1 0772229E_1997 0772229E 277 58.12275 3.105590
## 0772296C.Ana1 0772296C_2008 0772296C 163 67.48467 7.272728
## p_cs1 p_cs2 p_cs3 p_cs4 p_cs5 p_cs6
## 0750673Z.Ana1 0.000000 9.433962 23.11321 12.735849 18.86792 17.924528
## 0750711R.Ana1 1.503759 7.518797 30.82707 6.766917 24.81203 3.007519
## 0770922J.Ana1 0.000000 5.076142 29.94924 26.395939 18.27411 9.644671
## 0771663P.Ana1 0.000000 5.025125 29.14573 25.125628 23.11558 12.562814
## 0772229E.Ana1 1.444043 5.054151 21.29964 18.411552 18.05054 22.021660
## 0772296C.Ana1 0.000000 4.294478 16.56442 25.766870 22.69939 17.791410
## p_cs7 p_cs8 p_cs9 cep cep2 codedep codecom
## 0750673Z.Ana1 3.301887 11.320755 3.301887 1 0 75 110
## 0750711R.Ana1 3.007519 10.526316 12.030075 1 1 75 119
## 0770922J.Ana1 3.553299 4.568528 2.538071 1 1 77 108
## 0771663P.Ana1 1.005025 3.015075 1.005025 1 0 77 285
## 0772229E.Ana1 2.527076 7.220217 3.971119 1 0 77 284
## 0772296C.Ana1 1.840491 7.361963 3.680982 1 0 77 296
head(xmcoa)
## Axis1 Axis2 session patronyme
## 0750673Z.Ana1 -0.07772155 0.07455603 2003 COLBERT
## 0750711R.Ana1 -0.02875942 0.02443772 2014 HENRI BERGSON
## 0770922J.Ana1 -0.12615731 -0.00558298 2012 GASTON BACHELARD
## 0771663P.Ana1 -0.08780350 -0.01159594 1997 GEORGE SAND
## 0772229E.Ana1 -0.00757513 -0.05795082 1997 JEAN VILAR
## 0772296C.Ana1 -0.09695404 -0.04464101 2008 DE LA MARE CARREE
## id_etab numetab tot_inscrits psucc tx_btb
## 0750673Z.Ana1 0750673Z_2003 0750673Z 212 68.39623 2.758621
## 0750711R.Ana1 0750711R_2014 0750711R 133 67.66917 6.666667
## 0770922J.Ana1 0770922J_2012 0770922J 197 85.27919 26.190475
## 0771663P.Ana1 0771663P_1997 0771663P 199 76.38191 3.947369
## 0772229E.Ana1 0772229E_1997 0772229E 277 58.12275 3.105590
## 0772296C.Ana1 0772296C_2008 0772296C 163 67.48467 7.272728
## p_cs1 p_cs2 p_cs3 p_cs4 p_cs5 p_cs6
## 0750673Z.Ana1 0.000000 9.433962 23.11321 12.735849 18.86792 17.924528
## 0750711R.Ana1 1.503759 7.518797 30.82707 6.766917 24.81203 3.007519
## 0770922J.Ana1 0.000000 5.076142 29.94924 26.395939 18.27411 9.644671
## 0771663P.Ana1 0.000000 5.025125 29.14573 25.125628 23.11558 12.562814
## 0772229E.Ana1 1.444043 5.054151 21.29964 18.411552 18.05054 22.021660
## 0772296C.Ana1 0.000000 4.294478 16.56442 25.766870 22.69939 17.791410
## p_cs7 p_cs8 p_cs9 cep cep2 codedep codecom
## 0750673Z.Ana1 3.301887 11.320755 3.301887 1 0 75 110
## 0750711R.Ana1 3.007519 10.526316 12.030075 1 1 75 119
## 0770922J.Ana1 3.553299 4.568528 2.538071 1 1 77 108
## 0771663P.Ana1 1.005025 3.015075 1.005025 1 0 77 285
## 0772229E.Ana1 2.527076 7.220217 3.971119 1 0 77 284
## 0772296C.Ana1 1.840491 7.361963 3.680982 1 0 77 296
write.dta(xpta, "xpta.dta")
write.dta(xmcoa, "xmcoa.dta")
ppta <- ggplot(xpta, aes(CS1, CS2))
ppta + geom_path(aes(colour = session))
ppta + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
ppta + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
pmcoa <- ggplot(xmcoa, aes(Axis1, Axis2))
pmcoa + geom_path(aes(colour = session))
pmcoa + geom_path(aes(colour = session)) + facet_wrap( ~ numetab, ncol=10)
pmcoa + geom_path(aes(colour = session)) + facet_wrap( ~ patronyme, ncol=10)
collapse (mean) CS1 CS2, by( cep2 codedep session)
line CS1 session if cep==0, color(blue) || line CS1 session if cep==1, color(red) by(codedep)